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Montreal Canadiens 2021-22 Forward Ratings: Introduction

The Canadiens had a terrible season, but which players performed the best throughout the year?

Florida Panthers v Montreal Canadiens

As much as most Montreal Canadiens fans are trying to forget that last year ever happened, accept our first overall pick, and move on, I’ve put together an article series for the more morbid among us: 2021-22 season report cards.

I understand that the only people interested in a series like this also must enjoy true-crime podcasts, because this is a huge mess to untangle, but here’s the premise of the ratings.

I threw together a little model by which to judge the forwards. It only serves as a launching pad for the conversation, but for those who are interested, here’s how the model works.

I take into account several factors (which I’ll get into later) and find out how each player did compared to his teammates. There are 16 forwards that played over 250 minutes at five-on-five last year so for each stat a player will get up to 16 points, after which those points get weighted based on the importance of that particular stat.

The reason that I’m doing this as a ranking and not by using the raw numbers is because I feel that this makes the model more translatable to other teams and also rewards players for rising above their circumstances, even if that means they only rise to mediocrity.

All of the numbers are derived from five-on-five so as not to unduly reward someone for playing on the power play and vice versa for the penalty kill. So, without further ado, here are the stats and how they’re being weighted.

  1. Points per 60: In my humble opinion this is the most important aspect of being a forward and it’s the most heavily weighted. This is measuring the players' actual point production and isn’t an “on ice” stat. The original scale of 1-16 is weighted at times two. So if Player X is the best in this category they get 16 points, and that becomes 32 points after the weighting is factored in.
  2. Goals-for percentage: This stat is measured as a team stat, how does the team do when player X is on the ice. Some of the regular readers here will know that I’m usually more prone to expected metrics as opposed to measuring just the actual goals for and against at five-on-five. However, with this large of a sample size (the whole season), I think this stat becomes more important, which is why it’s weighted at times 1.75. So if player X leads the team in this metric they have a total of (16 x 1.75 =) 28 points available to them.
  3. Corsi-for percentage: This is an obvious one for any player playing any position. Corsi measures shot attempts for and against and then assigns a percentage of attempts one team controlled over the other while player X was on the ice; the share of shot attempts. It’s a good measure of puck possession as it’s basically counting shots but with a larger sample size. Whatever amount of points a player gets is multiplied by 1.5 meaning that the most points Player X can get is 24.
  4. Expected-goals-for percentage: Of course it’s important to rack up some decent chances for and limit those chances against. I like measuring both this and the actual goals so that a player who was a tad on the unlucky side with the actual goals has a chance to redeem themself, but it is weighted as less because there is truth to a players ability to “finish.” Player X has a total of 20 points available to them if they are the best on the team in this category.
  5. Defensive zone faceoffs per 60: This is the lowest-rated metric in our model. Basically I just added it to make sure that if a player was doing exceptionally well in the other categories because they were getting the easy deployment it would factor that sheltering into their record and hopefully judge players more holistically rather than just on their offence. Player X has a total of 16 points available to them by being number one in this category.

After looking up all of the stats, weighting them, and adding them together, I got a score out of 120 for each player. That score then gets turned into a percentage, and voila, we have our report card. The players will be ranked in the order that they finished and each article will come out ascending from worst to best according to this model.

Remember, discourse is appreciated here. After doing all of this, it isn’t necessarily the order that I would personally rank them in, but nonetheless it is (in my opinion) interesting as we prepare to head into a new season following some summer turnover.

Two more articles, for the 16th- and 15th-ranked forwards, will be published throughout the day.